code: def test_to_evaluation_dataset():
      import numpy as np
  
      source_uri = "test:/my/test/uri"
      source = TestDatasetSource._resolve(source_uri)
      df = pd.DataFrame([[1, 2, 3], [1, 2, 3]], columns=["a", "b", "c"])
      dataset = PandasDataset(
          df=df,
          source=source,
          targets="c",
          name="testname",
      )
      evaluation_dataset = dataset.to_evaluation_dataset()
      assert isinstance(evaluation_dataset, EvaluationDataset)
      assert evaluation_dataset.features_data.equals(
          df.drop("c", axis=1)
      )
      assert np.array_equal(
          evaluation_dataset.labels_data, df["c"].to_numpy()
      )
  
imports: import json
  import pandas
  import pytest
  import mlflow.data
  from mlflow.data.code_dataset_source import CodeDatasetSource
  from mlflow.data.delta_dataset_source import DeltaDatasetSource
  from mlflow.data.filesystem_dataset_source import FileSystemDatasetSource
  from mlflow.data.pandas_dataset import PandasDataset
  from mlflow.data.pyfunc_dataset_mixin import PyFuncInputsOutputs
  from mlflow.data.spark_dataset_source import SparkDatasetSource
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.types.schema import Schema
  from mlflow.types.utils import _infer_schema
  from tests.resources.data.dataset_source import TestDatasetSource
label_desc: Use image node_id 14 for calling a global function with example usage: test_to_evaluation_dataset() without return types